Computational Prediction for Antibiotics
Resistance Through Machine Learning and Pk/Pd
Analysis by Hyunjo Kim in Cohesive Journal of Microbiology & Infectious Disease
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Computational Prediction for Antibiotics Resistance Through Machine Learning and Pk/Pd Analysis_Crimson Publishers
1. Computational Prediction for Antibiotics
Resistance Through Machine Learning and Pk/Pd
Analysis
Hyunjo Kim1
* and Jaehoon Song2
1
School of pharmacy, Yonsei university, South Korea
2
ANSORP, CHA Bio Group at CHA Bio complex, South Korea
Introduction
Analarmingandpersistentriseinantibioticresistanceamongmanyimportantpathogenic
bacterial species poses one of the greatest contemporary challenges to the public health. The
treatment of bacterial infections is becoming increasingly ineffective due to rapid mutation
which leads to antibiotic resistant and resistant bacteria become more prevalent. As result the
existing antibiotics are gradually obsolete and again new drugs are needed to be designed for
the same threat. Recently, antibiotic resistance is a global health crisis linked to increased, and
often unrestricted, antibiotic use in humans and animals [1]. The rise of multiclass-antibiotics
resistant pathogens and the dearth of new antibiotic development place an existential strain
on successful infectious disease therapy. Breakthrough strategies that go beyond classical
antibiotic mechanisms are needed to combat this looming public health catastrophe.
Reconceptualizing antibiotic therapy in the richer context of the host-pathogen interaction is
required for innovative solutions. Here we review these relationships and their relevance to
antimicrobial resistance (AMR) trends witnessed in the clinical setting. This review highlights
the issues of enrichment and dissemination of antibiotics resistance genes (ARGs) [2,3] in
the environment, and also future needs in mitigating the spread of antibiotic resistance
in the environment, particularly under the planetary health perspective, i.e., the systems
that sustain or threaten human health. By defining specific virulence factors, the essence
of a pathogen, and pharmacologically neutralizing their activities, one can block disease
progression and sensitize microbes to immune clearance. Likewise, host-directed strategies
to boost phagocyte bactericidal activity, enhance leukocyte recruitment, or reverse pathogen-
induced immunosuppression seek to replicate the success of cancer immunotherapy in the
field of infectious diseases. The answer to the threat of multidrug-resistant pathogens lies in
current antibiotic paradigms. Furthermore, the rise of bacterial strains resistant to multiple
antibiotics is expected to dramatically limit treatment effectiveness, leading to potentially
incurable outbreaks [4]. In addition to new drug development efforts, there is an urgent need
for preclinical tools that are capable of effective and rapid detection of resistance, as culture-
based laboratory diagnostics test are usually time consuming and costly. One of the major
Crimson Publishers
Wings to the Research
Review Article
*1
Corresponding author: Hyunjo Kim,
School of pharmacy, Yonsei university,
Campus town, Songdo techno park, Incheon
city, South Korea
Submission: June 13, 2019
Published: June 24, 2019
Volume 2 - Issue 5
Howtocitethisarticle:HyunjoK, Jaehoon
S. Growth Factors in the Human Body: A
Conceptual Update. Cohesive J Microbiol
Infect Dis. 2(5). CJMI.000547.2019.
DOI: 10.31031/CJMI.2019.02.000547
Copyright@ Hyunjo Kim, This article is
distributed under the terms of the Creative
Commons Attribution 4.0 International
License, which permits unrestricted use
and redistribution provided that the
original author and source are credited.
ISSN: 2578-0190
1
Cohesive Journal of Microbiology & Infectious Disease
Abstract
Infection disease is a major cause of morbidity and mortality in the developing world. Antibiotics
resistance, which is predicted to rise in many countries worldwide, threatens infection treatment and
control. The machine learning can help to identify patients at higher risk of treatment failure in infection
diseases Closer monitoring of these patients may decrease treatment failure rates and prevent emergence
outbreak of antibiotic resistance. To identify features associated with treatment failure and to predict
which patients are at highest risk of antibiotics resistance this study was designed. The most predictive
model was forward stepwise selection, although most models performed at or above targeted value. We
employed a range of powerful machine learning tools to predict antibiotic resistance from whole genome
sequencing data for model strain. We used the presence or absence of genes, population structure and
isolation year of isolates as predictors, and could attain average precision and recall without prior
knowledge about the causal mechanisms. These results demonstrate the potential application of machine
learning methods as a diagnostic tool in healthcare settings.
Keywords: Infection disease; Antibiotics resistance; Machine learning model algorithm; Genome
analysis; Antibiotics resistance genes (ARGs); PK/PD analysis